Tetrabutylammonium fluoride (TBAF)-catalyzed addition of substituted trialkylsilylalkynes to aldehydes, ketones, and trifluoromethyl ketones

Herein we report that tetrabutylammonium fluoride (TBAF) is a very efficient catalyst for the addition of trialkylsilylalkynes to aldehydes, ketones, and trifluoromethyl ketones in THF solvent at room temperature. The reaction conditions are mild and operationally simple, and a variety of aryl functional groups, such as chloro, trifluoromethyl, bromo, and fluoro groups, are tolerated. Impressively, using our protocol, useful CF(3)-bearing tertiary propargylic alcohols can be synthesized. Product yields are generally better than or comparable to those in the literature. 1-Phenyl-2-trimethylsilyl acetylene, trimethyl ((4-(trifluoromethyl)phenyl)ethynyl)silane, 1-trimethylsilyl-1-hexyne, and trimethyl(thiophen-3-ylethynyl)silane underwent clean conversion to their corresponding propargylic alcohols as products under our conditions. Heterocyclic carbonyl compounds, such as furan-3-carboxaldehyde, thiophene-3-carboxaldehyde, and 2-pyridyl ketone, gave good yields of propargylic alcohols.     

Selective polarization mode excitation in InGaAs/GaAs microtubes

We report on selective polarization mode excitation in InGaAs/GaAs rolled-up microtubes. The microtubes are fabricated by selectively releasing a coherently strained InGaAs/GaAs quantum dot layer from its host GaAs substrate. An optical fiber abrupt taper is used to pick up the microtube, while an adiabatically tapered optical fiber is used to couple light into the resonant optical modes of the microtube. By varying the polarization of the light in the adiabatically tapered fiber both transverse electric and transverse magnetic modes are observed in the microtube. We also show that the microtube can be used as a red (0.6?μm) to infrared light (1.5?μm) optical-optical modulator taking advantage of the thermal-optical effect.     

Adjoint-based sensitivity analysis of flames

Simulation of chemically reacting flows using detailed chemistry introduces a large number of chemistry model parameters. While not all significantly affect the target outcomes of a simulation, the parameters that do are not always known a priori. In order to improve simulations for specified target outcomes, termed quantities of interest (QoIs), the sensitivity of these QoIs to the model parameters are needed. However, evaluating the sensitivities is computationally expensive, especially for complex fuels that may involve many parameters. For these simulations, the forward sensitivity method requires the solution of an additional number of governing equations proportional to the number of parameters. Here, an adjoint sensitivity approach is formulated where the computational cost scales as the number of QoIs and not the number of parameters. Specifically, adjoint equations are derived for laminar, incompressible, variable density reacting flow and applied to hydrogen flame simulations. From the solution of the corresponding adjoint equations, sensitivity of the QoIs to chemistry model parameters is calculated. The one-dimensional simulation results show that the adjoint sensitivity results closely match those of forward sensitivity methods, thus providing validation of the adjoint method. The two-dimensional simulation results indicate the most sensitive parameters for two QoIs, flame tip temperature and NOx emission. For these tests, the adjoint method reduces computational expense compared to forward sensitivity methods by a factor proportional to the number of QoIs over the number of parameters, here 2/172. Such savings can be more drastic for cases that involve complex fuels, such as combustion of jet fuel, requiring thousands of chemistry model parameters. Further, this sensitivity information can be used in development of experiments by pointing out which are the critical chemistry model parameters.     

Emerging trends in numerical simulations of combustion systems

Numerical simulations have played a vital role in the design of modern combustion systems. Over the last two decades, the focus of research has been on the development of the large eddy simulation (LES) approach, which leveraged the vast increase in computing power to dramatically improve predictive accuracy. Even with the anticipated increase in supercomputing capabilities, the use of LES in design is limited by its high computational cost. Moreover, to aid decision making, such LES computations have to be augmented to estimate underlying uncertainties in simulation components. At the same time, other changes are happening across industries that build or use combustion devices. While efficiency and emissions reduction are still the primary design objectives, reducing cost of operation by optimizing maintenance and repair is becoming an important segment of the enterprise. This latter quest is aided by the digitization of combustors, which allows collection and storage of operational data from a host of sensors over a fleet of devices. Moreover, several levels of computing including low-power hardware present on board the combustion systems are becoming available. Such large data sets create unique opportunities for design and maintenance if appropriate numerical tools are made available. As LES revolutionized computing-guided design by leveraging supercomputing, a new generation of numerical approaches is needed to utilize this vast amount of data and the varied nature of computing hardware. In this article, a review of emerging computational approaches for this heterogeneous data-driven environment is provided. A case is made that new but unconventional opportunities for physics-based combustion modeling exist in this realm.     

Stereoselective Total Synthesis of Pectinolides A,C, and H

A simple and straightforward stereoselective total synthesis of Pectinolides A, C, and H is described. The synthesis has been started from commercially available (+)‐diethyl l ‐tartrate and involves Ohira–Bestmann reaction, Corey–Bakshi–Shibata (CBS) reduction, and Still–Gennari olefination as key steps.     

Interpreting latent variables in factor models via convex optimization

Latent or unobserved phenomena pose a significant difficulty in data analysis as they induce complicated and confounding dependencies among a collection of observed variables. Factor analysis is a prominent multivariate statistical modeling approach that addresses this challenge by identifying the effects of (a small number of) latent variables on a set of observed variables. However, the latent variables in a factor model are purely mathematical objects that are derived from the observed phenomena, and they do not have any interpretation associated to them. A natural approach for attributing semantic information to the latent variables in a factor model is to obtain measurements of some additional plausibly useful covariates that may be related to the original set of observed variables, and to associate these auxiliary covariates to the latent variables. In this paper, we describe a systematic approach for identifying such associations. Our method is based on solving computationally tractable convex optimization problems, and it can be viewed as a generalization of the minimum-trace factor analysis procedure for fitting factor models via convex optimization. We analyze the theoretical consistency of our approach in a high-dimensional setting as well as its utility in practice via experimental demonstrations with real data.     

Shear-induced phase transitions in ternary polymer blends

We present a study of flow-induced phase transitions in microemulsion phases of ternary polymer blends. The results match qualitatively with the recent experimental observations on such systems but differ from the behavior expected and observed in the analogous system of surfactants. We rationalize this contrast from a molecular viewpoint suggesting that the interplay between polymer chain conformations and their flow deformations can lead to novel flow effects upon the phase, structural, and rheological behavior of multicomponent polymer systems.     

A new method of synthesis and antibacterial activity evaluation of piper amides

Several natural piper amides and their mimics were synthesized by developing a new strategy of amide formation. The piper amides were tested against both gram +ve and gram -ve bacterial strains and found that they are particularly more active against Staphylococcus aureus and Chromobacterium violaceum strains.     

ANTHEM: anatomically tailored hexagonal MRI

PURPOSE: This study aimed to investigate the use of anatomically tailored hexagonal sampling for scan-time and error reduction in MRI. MATERIALS AND METHODS: Anatomically tailored hexagonal MRI (ANTHEM), a method that combines hexagonal sampling with specific symmetry in anatomical geometry, is proposed. By using hexagonal sampling, aliasing artifacts are moved to regions where, due to the nature of the anatomy, aliasing is inconsequential. This can be used to either reduce scan time while maintaining spatial resolution or reduce residual errors in speedup techniques like UNFOLD and k-t BLAST/SENSE, which undersample k-space and unwrap fold-over artifacts during reconstruction. Computer simulations as well as phantom and volunteer studies were used to validate the theory. A simplified reconstruction algorithm for hexagonally sampled and subsampled k-space data was also used. RESULTS: A reduction in sampling density of 13.4% and 25% in each hexagonally sampled dimension was achieved for spherical and conical geometries without aliasing or reduction in spatial resolution. Optimal subsampling schemes that can be utilized by UNFOLD and k-t BLAST/SENSE were derived using hexagonal subsampling, which resulted in maximal, isotropic dispersal of the aliases. In combination with UNFOLD, ANTHEM was shown to move residual aliasing artifacts to the corners of the field of view, yielding reduced artifacts in CINE reconstructions. CONCLUSIONS: ANTHEM was successful in reducing acquisition time in conventional MRI and in reducing errors in UNFOLD imaging.